In [1]:
import io
import os
import sys
import types
import numpy as np
import pandas as pd
import altair as alt
import dropbox
import mysql.connector
from datetime import date
from datetime import datetime, timedelta 
import logging
In [2]:
%run MakeNBImportAvailable.ipynb
In [3]:
import GetMySQL
import knmi_gn_001 as gethdd

importing Jupyter notebook from GetMySQL.ipynb
importing Jupyter notebook from knmi_gn_001.ipynb
In [4]:
log_format = "%(asctime)s::%(levelname)s::%(name)s::"\
             "%(filename)s::%(lineno)d::%(message)s"
logging.basicConfig(filename='Energy.log', level='DEBUG', format=log_format)
In [5]:
color_gn = ['#D35400',
            '#7D3C98', 
            'steelblue', 
            'chartreuse',
            '#F4D03F',
            "#1696d2", 
                    "#d2d2d2",
                    "#000000", 
                    "#fdbf11", 
                    "#ec008b", 
                    "#55b748", 
                    "#5c5859", 
                    "#db2b27"]

Doel:
Geef overzicht van energieverbruik dit kalenderjaar en dit factuur jaar tov andere jaren
Plaats gasverbruik in relatie tot meetwaarden van het weer

Werkwijze: Database is minuut data, het duurt te lang om alle detaildata uit database te retrieven
Maak gebruik van file op schijf Raadpleeg de database voor ontbrekende dagen en overschrijf daarbij de laatste 2 dagen (nr is input)

In [6]:
def leesdatabase(nrdays,latest):
    """  
    """
    # overwrite last nr of days
    
    ndays = nrdays
    poi = (latest- timedelta( days=ndays)).strftime('%Y-%m-%d')
    poi = ''.join([poi,':'])
    poi = GetMySQL.SetPOI(poi)
    
    props = GetMySQL.readsettings()
    df_g = GetMySQL.getSQL(poi,"item0054",props,"gas")
    df_g = GetMySQL.RemoveReadingErrors_r1(df_g)
    df_EH = GetMySQL.getSQL(poi,"item0052",props,"EH")
    df_EH = GetMySQL.RemoveReadingErrors_r1(df_EH)
    df_EL = GetMySQL.getSQL(poi,"item0051",props,"EL")
    df_EL = GetMySQL.RemoveReadingErrors_r1(df_EL)
    df = pd.concat([df_g,df_EH,df_EL])
    
    
    return df
In [7]:
def berekenverbruik(df):
    df = df.reset_index()
    df['verbruik'] = df[['counter','variable']].groupby(['variable']).diff().shift(-1)
    df = df[~df['verbruik'].isnull()]
    df = df.groupby(['variable','dy'])['verbruik'].sum().reset_index()
    df['dy'] = pd.to_datetime(df['dy'])
    return df
In [8]:
def updateschijfdata(df_hist,df,fn):
    # verwijder dezelfde data punten uit de historische df
    df_hist = df_hist[~df_hist['dy'].isin(df['dy'])]
    df = df_hist.append(df)
    df = df.sort_values(['variable','dy'],ascending=True)
    dowrite=df.to_csv(fn)
    logging.info("data written to %s",fn)
    return df
In [9]:
def add_electricity(df):
    """ electricity comes with 2 categories: EH + EL 
    this functions combines these two and adds this data as one category to the df
    """
    electr = df[(df['variable']!="gas")&(df['variable']!="electricieit")]
    electr = electr[['verbruik','variable','dy']].groupby('dy').sum().reset_index()
    electr['variable']='electriciteit'
    df = pd.concat([df,electr])
    logging.info(df.head())
    logging.info("electricity of EH and EL combined in one category Electricity and added to the df")
    
    return df
In [10]:
def add_calendarinfo(df,firstmonth):
    df['yr'] = df['dy'].dt.year
    df['month'] =df['dy'].dt.month
    df['day']=df['dy'].dt.day
    df['year'] = 2024
    df['normaldate'] =pd.to_datetime(df[['year','month', 'day']],format='%Y-%m-%d')
    logging.info("calendar info added to df")
    
    df['cumul'] = df[['variable','dy','verbruik','yr']].groupby(['variable','yr']).cumsum() 
    
    # Factuur jaar FJ
    # corrigeer zodat eerste factuurmaand nr 1 wordt
    df['FJ_mnth'] = df['month']-(firstmonth-1)
    # corrigeer number maand in het jaar naar nummer maand in factuur jaar
    # als eerste maand 10 is, dan maand september wordt 12 : 9+(12-(10-1)) 
    df.loc[df['month']<firstmonth,['FJ_mnth']] = df.loc[df['month']<firstmonth,'month'].apply(lambda x: x+ (12-(firstmonth-1)))
    df['FJ_yr'] = df['yr']
    df.loc[df['month']>=firstmonth,['FJ_yr']] = df.loc[df['month']>=firstmonth,'yr'].apply(lambda x: x+1)
    df['FJ_cumul'] = df[['variable','FJ_yr','dy','verbruik']].groupby(['variable','FJ_yr']).cumsum()
    
    df['subtractyears'] = 0
    df.loc[df['month']>=firstmonth,['subtractyears']] = 1
    df['FJ_normaldate'] = df.apply(lambda x: x['normaldate']-pd.offsets.DateOffset(years=x['subtractyears']),axis=1)
    
    

        # Mathijs (2020-08-01)
    # Covid (thuiswerken 2020-03-01)
    # Vloerisolatie (2022-02-25)
    df['period']='1.pre-Covid'
    df.loc[df['dy']>='2020-03-01','period']='2.Covid'
    df.loc[df['dy']>='2021-09-01','period']='3.Mathijs'
    df.loc[df['dy']>='2022-02-26','period']='4.VloerIsolatie'
    df.loc[df['dy']>='2022-06-12','period']='5.ExtraDakIsolatie'
    df.loc[df['dy']>='2022-12-02','period']='6.CVnaar50dC'
    

    
    
    df['daysago'] = df['dy'].max() - df['dy'] 
    df['daysago'] = df['daysago'].dt.days 
    
    return df
In [11]:
def plot_kalenderjaar(df):
    # jaaroverzichten
    yrdata = df[['variable','yr','verbruik']].groupby(['variable','yr']).sum().reset_index()
    source = yrdata[yrdata['yr']!=2018]
    chart1=alt.Chart(source.reset_index(),title="Energieverbruik per jaar").mark_bar().encode(
        x='yr:O',
        y=alt.Y("verbruik:Q"),
        # color='yr:N',
        column='variable',
        color=alt.Color('yr:N',scale=alt.Scale(range=color_gn))
    ).interactive().properties(width=150,height=350)

    display(chart1)
    chart1.save('kalenderjaar.png')
    
    yrdata = pd.pivot_table(yrdata[yrdata['yr']!=2018], values = 'verbruik' , index = 'variable',columns = 'yr')
    # yrdata = yrdata.round(0)
   
    yrdata = yrdata.style.format(precision=0, na_rep='MISSING')

    display(yrdata)
In [12]:
def plotmedium(df,mediumstr):
    crrntyr = df['yr'].max()
    source1 = df[(df['variable']==mediumstr)&(df['yr']!=crrntyr)]
    
    chart1 = alt.Chart(source1,
                     title=mediumstr).mark_line(strokeWidth=1,point=True).encode(
        x=alt.X('month:N',axis=alt.Axis(title="Maand")),
        y=alt.Y("verbruik:Q",axis=alt.Axis(title="Verbruik")),
        color=alt.Color('yr:N',scale=alt.Scale(range=color_gn))).interactive().properties(width=300,height=250)
    
    source2 = df[(df['variable']==mediumstr)&(df['yr']==crrntyr)]
    chart2 = alt.Chart(source2,
                     title=mediumstr).mark_line(strokeWidth=3,point=True).encode(
        x=alt.X('month:N',axis=alt.Axis(title="Maand")),
        y=alt.Y("verbruik:Q",axis=alt.Axis(title="Verbruik")),
        color=alt.Color('yr:N',scale=alt.Scale(range=color_gn))).interactive().properties(width=300,height=250)
    
    
    
    return (chart1+chart2)
In [13]:
def plot_months(df):
    df_month = df.loc[df['yr']!=2018,['variable','yr','month','verbruik']].groupby(['variable','yr','month']).sum().reset_index()
    a1 = plotmedium(df_month,"gas")
    a2 = plotmedium(df_month,"electriciteit")
    a3 = plotmedium(df_month,"EL")
    a4 = plotmedium(df_month,"EH")
    chart_all=alt.vconcat(a1|a2,a3|a4)
    display(chart_all)
In [14]:
def plotcumuls(df,mediumstr):
    crrntyr = df['yr'].max()
    source1 = df[(df['variable']==mediumstr)&(df['yr']!=crrntyr)]
    
    chart1 = alt.Chart(source1,title=mediumstr).mark_line(
        strokeWidth=1).encode(
        x=alt.X('normaldate:T',
            axis=alt.Axis(title="datum")),
        y=alt.Y("cumul:Q",
                axis=alt.Axis(title="Verbruik")),
        # color='yr:N'
        color=alt.Color('yr:N',scale=alt.Scale(range=color_gn))
    ).properties(width=300,height=250).interactive()
    
    source2 = df[(df['variable']==mediumstr)&(df['yr']==crrntyr)]
    chart2 = alt.Chart(source2,title=mediumstr).mark_line(
        strokeWidth=3).encode(
        x=alt.X('normaldate:T'),
        y=alt.Y("cumul:Q"),
        color='yr:N')
    
    
    return (chart1+chart2)
In [15]:
def plotcumul(df):
    a1 = plotcumuls(df[df['yr']!=2018],"electriciteit")
    a2 = plotcumuls(df[df['yr']!=2018],"gas")
    a3 = plotcumuls(df[df['yr']!=2018],"EL")
    a4 = plotcumuls(df[df['yr']!=2018],"EH")
    chart_all=alt.vconcat(a1|a2,a3|a4)
    display(chart_all)
In [16]:
logging.info("read data from file")
fn = "dagelijks_energieverbuik.csv"
df_hist = pd.read_csv(fn, parse_dates=['dy'],
                      index_col = ['Unnamed: 0'])
In [17]:
latest = df_hist['dy'].max()
logging.info("last data point is: %s",str(latest))
nrdays = 10
df = leesdatabase(nrdays,latest)

POI is :  {'start': '2022-12-22', 'end': '2023-01-02'}
In [18]:
df.tail(10)
Out[18]:
counter variable dy
datetime
2023-01-01 12:20:00 7596.610 EL 2023-01-01
2023-01-01 12:21:00 7596.614 EL 2023-01-01
2023-01-01 12:22:00 7596.614 EL 2023-01-01
2023-01-01 12:23:00 7596.625 EL 2023-01-01
2023-01-01 12:24:00 7596.629 EL 2023-01-01
2023-01-01 12:25:00 7596.629 EL 2023-01-01
2023-01-01 12:26:00 7596.635 EL 2023-01-01
2023-01-01 12:27:00 7596.640 EL 2023-01-01
2023-01-01 12:28:00 7596.644 EL 2023-01-01
2023-01-01 12:29:00 7596.649 EL 2023-01-01
In [19]:
# add latest data from database to file
df = berekenverbruik(df)
In [20]:
# werk de file bij met laatste data
df = updateschijfdata(df_hist,df,fn)
In [21]:
# check dat variabele electriciteit nog niet bestaat 
df[df['variable']=='electriciteit']
Out[21]:
variable dy verbruik yr month day year normaldate cumul FJ_mnth FJ_yr FJ_cumul subtractyears FJ_normaldate period daysago
In [22]:
# before this step there should not be any line with variable electriciteit, maar kan per ongeluk in file geslopen zijn, dan handmatig eruit mikken en opnieuw script draaien
df = add_electricity(df)
In [23]:
df[df['variable']=='electriciteit']
Out[23]:
variable dy verbruik yr month day year normaldate cumul FJ_mnth FJ_yr FJ_cumul subtractyears FJ_normaldate period daysago
0 electriciteit 2018-11-25 6.604 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 electriciteit 2018-11-26 11.526 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 electriciteit 2018-11-27 9.133 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 electriciteit 2018-11-28 9.126 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 electriciteit 2018-11-29 8.582 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1458 electriciteit 2022-12-28 9.164 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1459 electriciteit 2022-12-29 10.247 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1460 electriciteit 2022-12-30 12.159 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1461 electriciteit 2022-12-31 16.195 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1462 electriciteit 2023-01-01 5.069 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

1463 rows × 16 columns

In [24]:
# introduce firstmonth of period when not comparing calendar year data
firstmonth = 6
df = add_calendarinfo(df,firstmonth)
In [25]:
plot_kalenderjaar(df)

INFO:tornado.access:200 GET / (::1) 6.20ms
INFO:tornado.access:200 GET /vega.js (::1) 5.45ms
INFO:tornado.access:200 GET /vega-lite.js (::1) 4.55ms
INFO:tornado.access:200 GET /vega-embed.js (::1) 4.71ms
yr 2019 2020 2021 2022 2023
variable          
EH 1957 2266 2444 2134 0
EL 1610 1629 1657 1935 5
electriciteit 3567 3894 4101 4069 5
gas 1433 1318 1739 1182 2
In [26]:
plot_months(df)
In [27]:
plotcumul(df)
In [28]:
def plotFJcumulmedium(df,mediumstr):
    
    crrntyr = df['FJ_yr'].max()
    source1 = df[(df['variable']==mediumstr)&(df['FJ_yr']!=crrntyr)]
    
    chart1 = alt.Chart(source1,title=mediumstr).mark_line(
        strokeWidth=1).encode(
        x=alt.X('FJ_normaldate:T',
            axis=alt.Axis(title="datum")),
        y=alt.Y("FJ_cumul:Q",
                axis=alt.Axis(title="Verbruik")),
        # color='FJ_yr:N'
        color=alt.Color('FJ_yr:N',scale=alt.Scale(range=color_gn))
    ).properties(width=300,height=250).interactive()
    
    source2 = df[(df['variable']==mediumstr)&(df['FJ_yr']==crrntyr)]
    chart2 = alt.Chart(source2,title=mediumstr).mark_line(
        strokeWidth=3).encode(
        x=alt.X('FJ_normaldate:T'),
        y=alt.Y("FJ_cumul:Q"),
        color='FJ_yr:N')
  
    return (chart1+chart2)
In [29]:
def plotFJcumuls(df):
    a1 = plotFJcumulmedium(df,"electriciteit")
    a2 = plotFJcumulmedium(df,"gas")
    a3 = plotFJcumulmedium(df,"EL")
    a4 = plotFJcumulmedium(df,"EH")
    chart_all=alt.vconcat(a1|a2,a3|a4)
    display(chart_all)
In [ ]:
In [30]:
plotFJcumuls(df[:-3])
In [31]:
def plotbars(df,mediumstr):
        chart1 = alt.Chart(df[df['variable']==mediumstr],
                     title=mediumstr).mark_bar(size=20).encode(
            x=alt.X('dy:T',
                    axis=alt.Axis(title="datum")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik")),
        # color='variable:N'
        color=alt.Color('variable:N',scale=alt.Scale(range=color_gn))
        ).properties(width=300,height=250)
        
        return (chart1)
In [32]:
def plot_lastdays(df, nrdays):
    datefrom = df['dy'].max()-timedelta(days=nrdays)
    df = df[df['dy']>datefrom]
    
    a1 = plotbars(df,"electriciteit")
    a2 = plotbars(df,"gas")
    a3 = plotbars(df,"EL")
    a4 = plotbars(df,"EH")
    chart_all=alt.vconcat(a1|a2,a3|a4)
    display(chart_all)
    
    tbl = pd.pivot_table(df,index='dy',values='verbruik',columns='variable')
    display(tbl)
In [33]:
plot_lastdays(df,10)
variable EH EL electriciteit gas
dy
2022-12-23 11.655 3.383 15.038 1.587
2022-12-24 0.000 16.716 16.716 0.646
2022-12-25 0.000 10.612 10.612 5.373
2022-12-26 0.000 9.752 9.752 0.811
2022-12-27 12.016 1.827 13.843 7.765
2022-12-28 7.544 1.620 9.164 4.691
2022-12-29 6.699 3.548 10.247 4.849
2022-12-30 8.575 3.584 12.159 5.763
2022-12-31 0.000 16.195 16.195 2.404
2023-01-01 0.000 5.069 5.069 1.507
In [34]:
def get_weatherdata(firstmonth):
    df = gethdd.retrieveHDD()
    df['month'] = df.index.month
    df['yr'] = df.index.year
    df['FJ_mnth'] = df['month']-(firstmonth-1)
    # df.loc[df['month']<10,['FJ_mnth']] = df.loc[df['month']<10,'month'].apply(lambda x: x+3)
    # als eerste maand 10 is, dan maand september wordt 12 : 9+(12-(10-1))
    df.loc[df['month']<firstmonth,['FJ_mnth']] = df.loc[df['month']<firstmonth,'month'].apply(lambda x: x+ (12-(firstmonth-1)))
    df['FJ_yr'] = df['yr']
    df.loc[df['month']>=firstmonth,['FJ_yr']] = df.loc[df['month']>=firstmonth,'yr'].apply(lambda x: x+1)
    
    df['FJ_cumul_hdd'] = df[['HDD','FJ_yr']].groupby(['FJ_yr']).cumsum()
    df['Teff'] = df['TG']*1/10 - 2/3*df['FG']*1/10
    
    df['day']=df.index.day
    df['year'] = 2024
    df['normaldate'] =pd.to_datetime(df[['year','month', 'day']],format='%Y-%m-%d')
    
    df['subtractyears'] = 0
    df.loc[df['month']>=firstmonth,['subtractyears']] = 1
    df['FJ_normaldate'] = df.apply(lambda x: x['normaldate']-pd.offsets.DateOffset(years=x['subtractyears']),axis=1)
    
    
    
    return df
In [35]:
df_hdd = get_weatherdata(firstmonth)
cols = ['HDD','FJ_cumul_hdd','TG','Teff']
df_hdd = df_hdd[df['dy'].min():]

read the csv file with knmi data  knmi_data.csv
last data point in csv file:  2022-12-30 00:00:00
The current time is : 2023-01-01 12:29:44.252130
The number of days since last datapoint is : 2
Update noodzakelijk
data saved to file :  knmi_data.csv
KNMI last datapoint is : 2022-12-30 00:00:00
Current time : 2023-01-01 12:29:44.795630
Elapsed time since  [hours] :  60
Nr rows to add :  2
              SQ      Q     DR    TG    FG    RH        HDD        dow
datetime                                                              
2022-12-28   0.0   65.0  137.0  85.0  57.0  47.0   9.300000  Wednesday
2022-12-29  18.0  157.0   43.0  92.0  63.0  31.0   9.000000   Thursday
2022-12-30   0.0  137.0   50.0  83.0  53.0  16.0   9.233333     Friday
2022-12-31   0.0    0.0    0.0   0.0   0.0   0.0  14.000000   Saturday
2023-01-01   0.0    0.0    0.0   0.0   0.0   0.0  14.000000     Sunday
In [36]:
df_hdd.tail()
Out[36]:
SQ Q DR TG FG RH HDD dow month yr FJ_mnth FJ_yr FJ_cumul_hdd Teff day year normaldate subtractyears FJ_normaldate
datetime
2022-12-28 0.0 65.0 137.0 85.0 57.0 47.0 9.300000 Wednesday 12 2022 7 2023 765.033333 4.700000 28 2024 2024-12-28 1 2023-12-28
2022-12-29 18.0 157.0 43.0 92.0 63.0 31.0 9.000000 Thursday 12 2022 7 2023 774.033333 5.000000 29 2024 2024-12-29 1 2023-12-29
2022-12-30 0.0 137.0 50.0 83.0 53.0 16.0 9.233333 Friday 12 2022 7 2023 783.266667 4.766667 30 2024 2024-12-30 1 2023-12-30
2022-12-31 0.0 0.0 0.0 0.0 0.0 0.0 14.000000 Saturday 12 2022 7 2023 797.266667 0.000000 31 2024 2024-12-31 1 2023-12-31
2023-01-01 0.0 0.0 0.0 0.0 0.0 0.0 14.000000 Sunday 1 2023 8 2023 811.266667 0.000000 1 2024 2024-01-01 0 2024-01-01
In [37]:
df = df.merge(df_hdd[cols],left_on="dy",right_on="datetime",how='right')
In [38]:
df.tail()
Out[38]:
variable dy verbruik yr month day year normaldate cumul FJ_mnth FJ_yr FJ_cumul subtractyears FJ_normaldate period daysago HDD FJ_cumul_hdd TG Teff
5859 electriciteit 2022-12-31 16.195 2022.0 12.0 31.0 2024.0 2024-12-31 4069.299 7.0 2023.0 2262.881 1.0 2023-12-31 6.CVnaar50dC 1.0 14.0 797.266667 0.0 0.0
5860 EH 2023-01-01 0.000 2023.0 1.0 1.0 2024.0 2024-01-01 0.000 8.0 2023.0 1084.988 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
5861 EL 2023-01-01 5.069 2023.0 1.0 1.0 2024.0 2024-01-01 5.069 8.0 2023.0 1182.962 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
5862 gas 2023-01-01 1.507 2023.0 1.0 1.0 2024.0 2024-01-01 1.507 8.0 2023.0 405.367 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
5863 electriciteit 2023-01-01 5.069 2023.0 1.0 1.0 2024.0 2024-01-01 5.069 8.0 2023.0 2267.950 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
In [39]:
source = df[df['verbruik']!=0]


chart1 = alt.Chart(source[(source['variable']=='gas')&
                      (source['period']!='6.CVNaar50dC')]).mark_point(clip=True).encode(
    x=alt.X('HDD:Q',axis=alt.Axis(title='HDD'),scale=alt.Scale(domain=(0, 26))),
    y=alt.Y('verbruik',axis=alt.Axis(title='verbruik'),scale=alt.Scale(domain=(0, 20))),
    # color='period:N',
    color=alt.Color('period:N',scale=alt.Scale(range=color_gn)),
    size='period'
).properties(width=600,height=400)


chart2 = alt.Chart(source[(source['variable']=='gas')&
                      (source['period']=='6.CVNaar50dC')]).mark_point(
    clip=True,filled=True,size=200).encode(
    x=alt.X('HDD:Q',axis=alt.Axis(title='HDD'),scale=alt.Scale(domain=(0, 26))),
    y=alt.Y('verbruik',axis=alt.Axis(title='verbruik'),scale=alt.Scale(domain=(0, 20))),
    # color='period:N',
    color=alt.Color('period:N',scale=alt.Scale(range=color_gn)),
    size='period'
).properties(width=600,height=400)

chart1+chart2   

/home/gert/env/lib/python3.9/site-packages/altair/utils/core.py:185: UserWarning: I don't know how to infer vegalite type from 'empty'.  Defaulting to nominal.
  warnings.warn(
Out[39]:
In [40]:
source = df[(df['variable']=='gas')&(df['daysago']>5)]
source = source[source['verbruik']!=0]
chart1 = alt.Chart(source).mark_point(clip=True).encode(
    x=alt.X('HDD:Q',axis=alt.Axis(title='HDD'),scale=alt.Scale(domain=(0, 26))),
    y=alt.Y('verbruik',axis=alt.Axis(title='verbruik'),scale=alt.Scale(domain=(0, 20))),
    # fill='period:N',
    fill=alt.Fill('period:N',scale=alt.Scale(range=color_gn)),
    size=alt.Size('period',scale=alt.Scale(range=[50,200]))
).interactive().properties(width=600,height=400)

# points = base.mark_point(clip=True,size=100).encode(
#         fill = alt.Fill('daysago:Q', scale = alt.Scale(scheme='plasma')),
#         shape = alt.Shape('dow:N',sort=cats),
#         size = alt.Size('daysago', scale=alt.Scale(range=[250, 10]))
#     )

chart2 = alt.Chart(df[(df['variable']=='gas')&(df['daysago']<=5)&(df['daysago']>0)]
                  ).mark_point(clip=True,size=300,fill='red').encode(
    x=alt.X('HDD:Q'),
    y=alt.Y('verbruik'),
    shape='daysago:N'
).properties(width=600,height=400)

text = chart2.mark_text(
        align='left',
        baseline='middle',
        dx=12
    ).encode(
        text='daysago'
    )

chart1 + chart2 +text
Out[40]:
In [41]:
source = df[(df['variable']=='gas')&(df['daysago']>5)]
source = source[source['verbruik']!=0]

color_gn = ['#D35400','#7D3C98', 'steelblue', 'chartreuse', '#F4D03F','green']

chart1 = alt.Chart(source).mark_point(clip=True).encode(
    x=alt.X('Teff:Q',axis=alt.Axis(title='Eff Temp'),scale=alt.Scale(domain=(-15, 26))),
    y=alt.Y('verbruik',axis=alt.Axis(title='verbruik'),scale=alt.Scale(domain=(0, 20))),
    # fill='period:N',
    # fill=alt.Fill('period:N',scale=alt.Scale(scheme='dark2')),
    fill=alt.Fill('period:N',scale=alt.Scale(range=color_gn)),
    size=alt.Size('period',scale=alt.Scale(range=[50,200]))
).interactive().properties(width=600,height=400)

# points = base.mark_point(clip=True,size=100).encode(
#         fill = alt.Fill('daysago:Q', scale = alt.Scale(scheme='plasma')),
#         shape = alt.Shape('dow:N',sort=cats),
#         size = alt.Size('daysago', scale=alt.Scale(range=[250, 10]))
#     )

chart2 = alt.Chart(df[(df['variable']=='gas')&(df['daysago']<=5)&(df['daysago']>0)]
                  ).mark_point(clip=True,size=300,fill='red').encode(
    x=alt.X('Teff:Q'),
    y=alt.Y('verbruik'),
    shape='daysago:N'
).properties(width=600,height=400)

text = chart2.mark_text(
        align='left',
        baseline='middle',
        dx=12
    ).encode(
        text='daysago'
    )

chart1 + chart2 +text
Out[41]:
In [42]:
df_hdd.tail()
Out[42]:
SQ Q DR TG FG RH HDD dow month yr FJ_mnth FJ_yr FJ_cumul_hdd Teff day year normaldate subtractyears FJ_normaldate
datetime
2022-12-28 0.0 65.0 137.0 85.0 57.0 47.0 9.300000 Wednesday 12 2022 7 2023 765.033333 4.700000 28 2024 2024-12-28 1 2023-12-28
2022-12-29 18.0 157.0 43.0 92.0 63.0 31.0 9.000000 Thursday 12 2022 7 2023 774.033333 5.000000 29 2024 2024-12-29 1 2023-12-29
2022-12-30 0.0 137.0 50.0 83.0 53.0 16.0 9.233333 Friday 12 2022 7 2023 783.266667 4.766667 30 2024 2024-12-30 1 2023-12-30
2022-12-31 0.0 0.0 0.0 0.0 0.0 0.0 14.000000 Saturday 12 2022 7 2023 797.266667 0.000000 31 2024 2024-12-31 1 2023-12-31
2023-01-01 0.0 0.0 0.0 0.0 0.0 0.0 14.000000 Sunday 1 2023 8 2023 811.266667 0.000000 1 2024 2024-01-01 0 2024-01-01
In [43]:
def plotFJcumulHDD(df):
    crrntyr = df['FJ_yr'].max()
    source1 = df[(df['FJ_yr']!=crrntyr)]
    
    chart1 = alt.Chart(source1,title="HDD").mark_line(
        strokeWidth=1).encode(
        x=alt.X('FJ_normaldate:T',
            axis=alt.Axis(title="datum")),
        y=alt.Y("FJ_cumul_hdd:Q",
                axis=alt.Axis(title="Verbruik")),
        color='FJ_yr:N'
    ).properties(width=300,height=250).interactive()
    
    source2 = df[(df['FJ_yr']==crrntyr)]
    chart2 = alt.Chart(source2).mark_line(
        strokeWidth=3).encode(
        x=alt.X('FJ_normaldate:T'),
        y=alt.Y("FJ_cumul_hdd:Q"),
        color='FJ_yr:N')
    
    return (chart1+chart2)
In [ ]:
In [44]:
today = date.today()
df[df['dy']!=today.strftime("%Y-%m-%d")].tail()
Out[44]:
variable dy verbruik yr month day year normaldate cumul FJ_mnth FJ_yr FJ_cumul subtractyears FJ_normaldate period daysago HDD FJ_cumul_hdd TG Teff
5855 electriciteit 2022-12-30 12.159 2022.0 12.0 30.0 2024.0 2024-12-30 4053.104 7.0 2023.0 2246.686 1.0 2023-12-30 6.CVnaar50dC 2.0 9.233333 783.266667 83.0 4.766667
5856 EH 2022-12-31 0.000 2022.0 12.0 31.0 2024.0 2024-12-31 2133.919 7.0 2023.0 1084.988 1.0 2023-12-31 6.CVnaar50dC 1.0 14.000000 797.266667 0.0 0.000000
5857 EL 2022-12-31 16.195 2022.0 12.0 31.0 2024.0 2024-12-31 1935.380 7.0 2023.0 1177.893 1.0 2023-12-31 6.CVnaar50dC 1.0 14.000000 797.266667 0.0 0.000000
5858 gas 2022-12-31 2.404 2022.0 12.0 31.0 2024.0 2024-12-31 1182.297 7.0 2023.0 403.860 1.0 2023-12-31 6.CVnaar50dC 1.0 14.000000 797.266667 0.0 0.000000
5859 electriciteit 2022-12-31 16.195 2022.0 12.0 31.0 2024.0 2024-12-31 4069.299 7.0 2023.0 2262.881 1.0 2023-12-31 6.CVnaar50dC 1.0 14.000000 797.266667 0.0 0.000000
In [45]:
display(plotFJcumulHDD(df_hdd[df_hdd.index.date!=datetime.now().date()])|plotFJcumulmedium(df[df['dy']!=today.strftime("%Y-%m-%d")],"gas"))
In [46]:
df.columns
Out[46]:
Index(['variable', 'dy', 'verbruik', 'yr', 'month', 'day', 'year',
       'normaldate', 'cumul', 'FJ_mnth', 'FJ_yr', 'FJ_cumul', 'subtractyears',
       'FJ_normaldate', 'period', 'daysago', 'HDD', 'FJ_cumul_hdd', 'TG',
       'Teff'],
      dtype='object')
In [47]:
t2 = df.iloc[-1]['dy']
t1 = df[(df['yr']==2022)&(df['FJ_mnth']==1)&(df['day']==1)]['dy'].tolist()[0]
In [48]:
t2-t1
Out[48]:
Timedelta('214 days 00:00:00')
In [49]:
df.tail()
Out[49]:
variable dy verbruik yr month day year normaldate cumul FJ_mnth FJ_yr FJ_cumul subtractyears FJ_normaldate period daysago HDD FJ_cumul_hdd TG Teff
5859 electriciteit 2022-12-31 16.195 2022.0 12.0 31.0 2024.0 2024-12-31 4069.299 7.0 2023.0 2262.881 1.0 2023-12-31 6.CVnaar50dC 1.0 14.0 797.266667 0.0 0.0
5860 EH 2023-01-01 0.000 2023.0 1.0 1.0 2024.0 2024-01-01 0.000 8.0 2023.0 1084.988 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
5861 EL 2023-01-01 5.069 2023.0 1.0 1.0 2024.0 2024-01-01 5.069 8.0 2023.0 1182.962 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
5862 gas 2023-01-01 1.507 2023.0 1.0 1.0 2024.0 2024-01-01 1.507 8.0 2023.0 405.367 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
5863 electriciteit 2023-01-01 5.069 2023.0 1.0 1.0 2024.0 2024-01-01 5.069 8.0 2023.0 2267.950 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0
In [50]:
mnth_hdd = df[df['variable']=='gas'].groupby(['period','month']).sum().reset_index()
In [51]:
mnth_hdd.tail(10)
Out[51]:
period month verbruik yr day year cumul FJ_mnth FJ_yr FJ_cumul subtractyears daysago HDD FJ_cumul_hdd TG Teff
33 4.VloerIsolatie 6.0 5.861 22242.0 66.0 22264.0 8598.696 11.0 22253.0 35.889 11.0 2299.0 11.966667 94.766667 1694.0 145.466667
34 5.ExtraDakIsolatie 6.0 8.891 38418.0 399.0 38456.0 14994.259 19.0 38437.0 203.956 19.0 3686.0 5.033333 301.166667 3372.0 310.533333
35 5.ExtraDakIsolatie 7.0 9.154 62682.0 496.0 62744.0 24772.736 62.0 62713.0 641.189 31.0 5239.0 0.933333 544.733333 5590.0 512.400000
36 5.ExtraDakIsolatie 8.0 10.317 62682.0 496.0 62744.0 25042.529 93.0 62713.0 910.982 31.0 4278.0 0.300000 557.133333 6050.0 566.333333
37 5.ExtraDakIsolatie 9.0 16.933 60660.0 465.0 60720.0 24567.655 120.0 60690.0 1214.545 30.0 3225.0 70.800000 1106.933333 4252.0 385.133333
38 5.ExtraDakIsolatie 10.0 26.348 62682.0 496.0 62744.0 26118.411 155.0 62713.0 1986.864 31.0 2387.0 85.833333 4394.833333 4024.0 350.866667
39 5.ExtraDakIsolatie 11.0 108.855 60660.0 465.0 60720.0 26902.098 180.0 60690.0 3548.988 30.0 1395.0 231.966667 8374.233333 2493.0 188.033333
40 5.ExtraDakIsolatie 12.0 6.738 2022.0 1.0 2024.0 971.534 7.0 2023.0 193.097 1.0 31.0 9.966667 416.800000 53.0 4.033333
41 6.CVnaar50dC 1.0 1.507 2023.0 1.0 2024.0 1.507 8.0 2023.0 405.367 0.0 0.0 14.000000 811.266667 0.0 0.000000
42 6.CVnaar50dC 12.0 210.763 60660.0 495.0 60720.0 32848.724 210.0 60690.0 9495.614 30.0 465.0 380.466667 18907.933333 948.0 39.533333
In [52]:
mnth_hdd[mnth_hdd['HDD']<10]
Out[52]:
period month verbruik yr day year cumul FJ_mnth FJ_yr FJ_cumul subtractyears daysago HDD FJ_cumul_hdd TG Teff
5 1.pre-Covid 6.0 19.191 60570.0 465.0 60720.0 24705.710 30.0 60600.0 308.030 30.0 38865.0 9.033333 194.300000 5518.0 497.666667
6 1.pre-Covid 7.0 11.948 62589.0 496.0 62744.0 26030.977 62.0 62620.0 820.041 31.0 39215.0 8.933333 490.533333 5745.0 525.300000
7 1.pre-Covid 8.0 18.476 62589.0 496.0 62744.0 26476.593 93.0 62620.0 1265.657 31.0 38254.0 2.966667 606.233333 5633.0 512.700000
34 5.ExtraDakIsolatie 6.0 8.891 38418.0 399.0 38456.0 14994.259 19.0 38437.0 203.956 19.0 3686.0 5.033333 301.166667 3372.0 310.533333
35 5.ExtraDakIsolatie 7.0 9.154 62682.0 496.0 62744.0 24772.736 62.0 62713.0 641.189 31.0 5239.0 0.933333 544.733333 5590.0 512.400000
36 5.ExtraDakIsolatie 8.0 10.317 62682.0 496.0 62744.0 25042.529 93.0 62713.0 910.982 31.0 4278.0 0.300000 557.133333 6050.0 566.333333
40 5.ExtraDakIsolatie 12.0 6.738 2022.0 1.0 2024.0 971.534 7.0 2023.0 193.097 1.0 31.0 9.966667 416.800000 53.0 4.033333
In [53]:
chart1 = alt.Chart(mnth_hdd[mnth_hdd['period']!='6.CVnaar50dC'],title="HDD_month").mark_point(
        size=200).encode(
        x=alt.X('HDD:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik")),
        fill=alt.Fill('period:N',scale=alt.Scale(range=color_gn)),
        shape = 'month:N'
    ).properties(width=600,height=400).interactive()

chart2=alt.Chart(mnth_hdd[mnth_hdd['period']=='6.CVnaar50dC']).mark_point(
        size=500).encode(
        x=alt.X('HDD:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik")),
        fill='period:N',
    shape = 'month:N'
    )


chart = chart1 + chart2
In [54]:
mnth_hdd[mnth_hdd['period']=='6.CVnaar50dC']
Out[54]:
period month verbruik yr day year cumul FJ_mnth FJ_yr FJ_cumul subtractyears daysago HDD FJ_cumul_hdd TG Teff
41 6.CVnaar50dC 1.0 1.507 2023.0 1.0 2024.0 1.507 8.0 2023.0 405.367 0.0 0.0 14.000000 811.266667 0.0 0.000000
42 6.CVnaar50dC 12.0 210.763 60660.0 495.0 60720.0 32848.724 210.0 60690.0 9495.614 30.0 465.0 380.466667 18907.933333 948.0 39.533333
In [55]:
chart
Out[55]:
In [ ]:

Week hdd verbruik¶

In [56]:
df['wk'] = df['dy'].dt.isocalendar().week
In [57]:
df.tail()
Out[57]:
variable dy verbruik yr month day year normaldate cumul FJ_mnth ... FJ_cumul subtractyears FJ_normaldate period daysago HDD FJ_cumul_hdd TG Teff wk
5859 electriciteit 2022-12-31 16.195 2022.0 12.0 31.0 2024.0 2024-12-31 4069.299 7.0 ... 2262.881 1.0 2023-12-31 6.CVnaar50dC 1.0 14.0 797.266667 0.0 0.0 52
5860 EH 2023-01-01 0.000 2023.0 1.0 1.0 2024.0 2024-01-01 0.000 8.0 ... 1084.988 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0 52
5861 EL 2023-01-01 5.069 2023.0 1.0 1.0 2024.0 2024-01-01 5.069 8.0 ... 1182.962 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0 52
5862 gas 2023-01-01 1.507 2023.0 1.0 1.0 2024.0 2024-01-01 1.507 8.0 ... 405.367 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0 52
5863 electriciteit 2023-01-01 5.069 2023.0 1.0 1.0 2024.0 2024-01-01 5.069 8.0 ... 2267.950 0.0 2024-01-01 6.CVnaar50dC 0.0 14.0 811.266667 0.0 0.0 52

5 rows × 21 columns

In [58]:
wk_hdd = df[df['variable']=='gas'].groupby(['period','yr','wk']).sum().reset_index()
In [59]:
wk_hdd.tail()
Out[59]:
period yr wk verbruik month day year cumul FJ_mnth FJ_yr FJ_cumul subtractyears daysago HDD FJ_cumul_hdd TG Teff
213 6.CVnaar50dC 2022.0 49 53.998 84.0 56.0 14168.0 7177.828 49.0 14161.0 1728.769 7.0 168.0 93.100000 3580.400000 139.0 4.900000
214 6.CVnaar50dC 2022.0 50 75.517 84.0 105.0 14168.0 7661.788 49.0 14161.0 2212.729 7.0 119.0 124.933333 4385.800000 -194.0 -26.933333
215 6.CVnaar50dC 2022.0 51 30.288 84.0 154.0 14168.0 8019.927 49.0 14161.0 2570.868 7.0 70.0 53.633333 4982.866667 581.0 44.366667
216 6.CVnaar50dC 2022.0 52 26.283 72.0 171.0 12144.0 7027.016 42.0 12138.0 2356.394 6.0 21.0 63.533333 4619.333333 378.0 20.466667
217 6.CVnaar50dC 2023.0 52 1.507 1.0 1.0 2024.0 1.507 8.0 2023.0 405.367 0.0 0.0 14.000000 811.266667 0.0 0.000000
In [60]:
chart1 = alt.Chart(wk_hdd[wk_hdd['period']!='6.CVnaar50dC'],title="HDD_week").mark_point(
        size=100).encode(
        x=alt.X('HDD:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik"),scale=alt.Scale(domain=[0, 120])),    
        fill=alt.Fill('period:N',scale=alt.Scale(range=color_gn)),
        shape = 'wk:N'
    ).properties(width=600,height=400).interactive()


chart2 = alt.Chart(wk_hdd[wk_hdd['period']=='6.CVnaar50dC']).mark_point(
        size=800).encode(
        x=alt.X('HDD:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik")),
        fill=alt.Fill('period:N',scale=alt.Scale(range=color_gn)),
        shape = 'wk:N'
    )



chart = chart1 + chart2
In [61]:
chart
Out[61]:
In [62]:
chart1 = alt.Chart(wk_hdd[wk_hdd['period']=='5.ExtraDakIsolatie'],title="HDD_week").mark_point(
        size=100).encode(
        x=alt.X('HDD:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik"),scale=alt.Scale(domain=[0, 120])),    
        fill=alt.Fill('period:N',scale=alt.Scale(range=color_gn)),
        shape = 'wk:N'
    ).properties(width=600,height=400).interactive()


chart2 = alt.Chart(wk_hdd[wk_hdd['period']=='6.CVnaar50dC']).mark_point(
        size=800).encode(
        x=alt.X('HDD:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik")),
        fill=alt.Fill('period:N',scale=alt.Scale(range=color_gn)),
        shape = 'wk:N'
    )



chart = chart1 + chart2
chart
Out[62]:
In [63]:
wk_hdd[wk_hdd['period']=='6.CVnaar50dC']
Out[63]:
period yr wk verbruik month day year cumul FJ_mnth FJ_yr FJ_cumul subtractyears daysago HDD FJ_cumul_hdd TG Teff
212 6.CVnaar50dC 2022.0 48 24.677 36.0 9.0 6072.0 2962.165 21.0 6069.0 626.854 3.0 87.0 45.266667 1339.533333 44.0 -3.266667
213 6.CVnaar50dC 2022.0 49 53.998 84.0 56.0 14168.0 7177.828 49.0 14161.0 1728.769 7.0 168.0 93.100000 3580.400000 139.0 4.900000
214 6.CVnaar50dC 2022.0 50 75.517 84.0 105.0 14168.0 7661.788 49.0 14161.0 2212.729 7.0 119.0 124.933333 4385.800000 -194.0 -26.933333
215 6.CVnaar50dC 2022.0 51 30.288 84.0 154.0 14168.0 8019.927 49.0 14161.0 2570.868 7.0 70.0 53.633333 4982.866667 581.0 44.366667
216 6.CVnaar50dC 2022.0 52 26.283 72.0 171.0 12144.0 7027.016 42.0 12138.0 2356.394 6.0 21.0 63.533333 4619.333333 378.0 20.466667
217 6.CVnaar50dC 2023.0 52 1.507 1.0 1.0 2024.0 1.507 8.0 2023.0 405.367 0.0 0.0 14.000000 811.266667 0.0 0.000000
In [64]:
import numpy as np
from sklearn.linear_model import LinearRegression
In [65]:
model = LinearRegression()
In [66]:
x = np.array(wk_hdd[wk_hdd['period']=='6.CVnaar50dC']['HDD']).reshape(-1,1)
y = np.array(wk_hdd[wk_hdd['period']=='6.CVnaar50dC']['verbruik']).reshape(-1,1)
In [67]:
x.reshape((-1, 1))
Out[67]:
array([[ 45.26666667],
       [ 93.1       ],
       [124.93333333],
       [ 53.63333333],
       [ 63.53333333],
       [ 14.        ]])
In [68]:
model = LinearRegression().fit(x, y)
In [69]:
r_sq = model.score(x, y)
In [70]:
r_sq
Out[70]:
0.9771084153839216
In [71]:
print(f"intercept: {model.intercept_}")

intercept: [-7.91339632]
In [72]:
print(f"slope: {model.coef_}")

slope: [[0.65848499]]
In [ ]:
In [73]:
mask = (df['variable']=='gas')&(df['daysago']>0)&(df['daysago']<7)
lastweek = df[mask].copy()
In [74]:
lastweek['cumul_hdd_wk'] = lastweek['HDD'].cumsum()
lastweek['cumul_verbruik_wk'] = lastweek['verbruik'].cumsum()
In [75]:
lastweek
Out[75]:
variable dy verbruik yr month day year normaldate cumul FJ_mnth ... FJ_normaldate period daysago HDD FJ_cumul_hdd TG Teff wk cumul_hdd_wk cumul_verbruik_wk
5838 gas 2022-12-26 0.811 2022.0 12.0 26.0 2024.0 2024-12-26 1156.825 7.0 ... 2023-12-26 6.CVnaar50dC 6.0 10.266667 744.000000 66.0 3.733333 52 10.266667 0.811
5842 gas 2022-12-27 7.765 2022.0 12.0 27.0 2024.0 2024-12-27 1164.590 7.0 ... 2023-12-27 6.CVnaar50dC 5.0 11.733333 755.733333 52.0 2.266667 52 22.000000 8.576
5846 gas 2022-12-28 4.691 2022.0 12.0 28.0 2024.0 2024-12-28 1169.281 7.0 ... 2023-12-28 6.CVnaar50dC 4.0 9.300000 765.033333 85.0 4.700000 52 31.300000 13.267
5850 gas 2022-12-29 4.849 2022.0 12.0 29.0 2024.0 2024-12-29 1174.130 7.0 ... 2023-12-29 6.CVnaar50dC 3.0 9.000000 774.033333 92.0 5.000000 52 40.300000 18.116
5854 gas 2022-12-30 5.763 2022.0 12.0 30.0 2024.0 2024-12-30 1179.893 7.0 ... 2023-12-30 6.CVnaar50dC 2.0 9.233333 783.266667 83.0 4.766667 52 49.533333 23.879
5858 gas 2022-12-31 2.404 2022.0 12.0 31.0 2024.0 2024-12-31 1182.297 7.0 ... 2023-12-31 6.CVnaar50dC 1.0 14.000000 797.266667 0.0 0.000000 52 63.533333 26.283

6 rows × 23 columns

In [76]:
chart1 = alt.Chart(lastweek,title="Previous 7 days").mark_point(
        size=100).encode(
        x=alt.X('HDD:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("verbruik:Q",
                axis=alt.Axis(title="Verbruik")),
        fill=alt.Fill('daysago:N',scale=alt.Scale(range=color_gn))
).properties(width=350,height=400).interactive()

chart2 = alt.Chart(lastweek,title="Previous 7 days").mark_point(
        size=100).encode(
        x=alt.X('cumul_hdd_wk:Q',
            axis=alt.Axis(title="HDD")),
        y=alt.Y("cumul_verbruik_wk:Q",
                axis=alt.Axis(title="Verbruik")),
        fill=alt.Fill('daysago:N',scale=alt.Scale(range=color_gn))
).properties(width=350,height=400).interactive()



chart1 | chart2
Out[76]:
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